Text Summarization Research Articles

Clustering-based topic modeling for biomedical documents extractive text summarization

AbstractThe increasing volume of electronic text, especially in the biomedical domain, requires automatic text summarization (ATS) to help researchers navigate and find relevant information. This study proposes an unsupervised extractive ATS method to improve the quality of biomedical document summaries by focusing on subtopic diversity. The method integrates topic modeling and clustering with bidirectional encoder representation from transformers (BERT). To evaluate the effectiveness of the proposed study, it tested on a new corpus of 200 biomedical research papers from Biomed Central. The results were evaluated using the ROUGE metric and qualitative evaluation by medical experts. The ROUGE metric yielded scores of 0.4838 (Rouge-1), 0.2174 (Rouge-2), and 0.2206 (Rouge-L), while the qualitative evaluation achieved an average score of 4.10, 4.06, 3.55, 4.0, and 4.0 for completeness, relevance, conciseness, informativity, and readability, respectively. The results demonstrate the effectiveness of the proposed method in summarizing long medical documents.

Diversifying Multi-Head Attention in the Transformer Model

Recent studies have shown that, due to redundancy, some heads of the Transformer model can be pruned without diminishing the efficiency of the model. In this paper, we propose a constrained optimization algorithm based on Hebbian learning, which trains specific layers in the Transformer architecture in order to enforce diversification between the different heads in the multi-head attention module. The diversification of the heads is achieved through a single-layer feed-forward neural network that is added to the Transformer architecture and is trained with the proposed algorithm. We utilize the algorithm in three different architectural variations of the baseline Transformer model. In addition to the diversification of the heads, the proposed methodology can be used to prune the heads that capture redundant information. Experiments on diverse NLP tasks, including machine translation, text summarization, question answering and large language modeling, show that our proposed approach consistently improves the performance of baseline Transformer models.

NewsZoom (focus on the important): Text summarization of News Articles based on named entity recognition using Spacy library

The study discusses the importance of summarization in dealing with a large amount of data available on the internet. The study used a deep-learning algorithm based on functions from the spacy library in Python to summarize news articles and evaluated the impact of named entity recognition on the summarization process. The study assessed different datasets from CNN-DailyMail and the BBC (entertainment articles) and found that the proposed method based on named entity recognition showed significant improvement in recall, precision, and F-score compared to the word frequency method. The study also observed that the articles from CNN- DailyMail were longer, with an average of 551 words and 28 sentences, compared to the BBC (entertainment articles), which had an average of 190 words and 12 sentences. The evaluation results showed that the proposed method based onnamed entity recognition performed better on the shorter articles from the BBC, indicating that the method was more effective in summarizing shorter texts. In summary, the study highlighted the importance of summarization in dealing with a large amount of data available on the internet. It showed that named entity recognition can significantly improve the effectiveness of the summarization process. The study alsoobserved that the proposed method was more effective in summarizing shorter texts Keywords: Spacy library, entity recognition, Summarization, Deep-Learning, CNN-DailyMail

Transformer models in text summarization

Abstract. Text summarization represents a core research topic within the realm of natural language processing and is extensively applied across various domains, including journalism, library administration, information gathering, among others. With the development of deep learning, especially the proposed Transformer structure has greatly promoted the development of text summaries. This paper reviews the recent progress in Transformer-based text summarization methods. It begins with an overview of traditional text summarization techniques. The paper then delves into the advantages of Transformer models for text summarization, such as their ability to understand global context, dynamically allocate weights, and accelerate parallel computation. Text summarization models are classified into several types, such as abstraction-based, extraction-based, and those leveraging large language models. Notably, Models like PEGASUS, BERT, and HETFORMER have emerged as leading examples in this field. In addition, the effectiveness, advantages and disadvantages of these models are analyzed.

Survey Paper on Smart Note Taker

The Smart Note Taker is an innovative tool designed to maximize the entire process of note taking. By doing so, it enables users to make quick and effective notes either handwritten or voice-activated. Using handwriting recognition and voice-to-text technology, the digital tool transcribes handwritten or spoken information as editable digital text. With cloudbased storage and real-time synchronization, users will be able to access and manage their notes anywhere, at any time, and on multiple devices, thus ensuring the smooth integration of the platform into the workflows of the user. In addition, multimedia is supported, and users are enabled to add images, audio, and video to their notes. Moreover, the AI-powered features, such as automatic summarization, content relevance, and suggestions, help users increase productivity. The Smart Note Taker might be taken to be prepared for students, for working professionals and visually impaired users. It should, therefore, be most adapted to the needs of modern note-taking in terms of being convenient, flexible, and accessible

Targeting COVID-19 and Human Resources for Health News Information Extraction: Algorithm Development and Validation.

Global pandemics like COVID-19 put a high amount of strain on health care systems and health workers worldwide. These crises generate a vast amount of news information published online across the globe. This extensive corpus of articles has the potential to provide valuable insights into the nature of ongoing events and guide interventions and policies. However, the sheer volume of information is beyond the capacity of human experts to process and analyze effectively. The aim of this study was to explore how natural language processing (NLP) can be leveraged to build a system that allows for quick analysis of a high volume of news articles. Along with this, the objective was to create a workflow comprising human-computer symbiosis to derive valuable insights to support health workforce strategic policy dialogue, advocacy, and decision-making. We conducted a review of open-source news coverage from January 2020 to June 2022 on COVID-19 and its impacts on the health workforce from the World Health Organization (WHO) Epidemic Intelligence from Open Sources (EIOS) by synergizing NLP models, including classification and extractive summarization, and human-generated analyses. Our DeepCovid system was trained on 2.8 million news articles in English from more than 3000 internet sources across hundreds of jurisdictions. Rules-based classification with hand-designed rules narrowed the data set to 8508 articles with high relevancy confirmed in the human-led evaluation. DeepCovid's automated information targeting component reached a very strong binary classification performance of 98.98 for the area under the receiver operating characteristic curve (ROC-AUC) and 47.21 for the area under the precision recall curve (PR-AUC). Its information extraction component attained good performance in automatic extractive summarization with a mean Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score of 47.76. DeepCovid's final summaries were used by human experts to write reports on the COVID-19 pandemic. It is feasible to synergize high-performing NLP models and human-generated analyses to benefit open-source health workforce intelligence. The DeepCovid approach can contribute to an agile and timely global view, providing complementary information to scientific literature.

Optimal Keyword Selection by Hybrid Optimization with Itemset Mining for Text Summarization in Biomedical Sector

Biomedical literature is growing exponentially, creating challenges for researchers and clinicians to access relevant information efficiently. Automatic biomedical text summarization is a promising solution to this issue, allowing the extraction of essential information while reducing redundancy. Traditional summarization techniques often rely on shallow linguistic features or simple term frequencies, which fail to capture the complex relationships in biomedical texts. This paper explores advanced methods for biomedical text summarization, including graph-based approaches, frequent item set mining, and deep learning models such as BERT. The proposed framework introduces a hybrid optimization technique combining Colliding Bodies Optimization (CBO) and Cuckoo Search Optimization (CSO) for optimal keyword selection, alongside a Recurrent Neural Network (RNN) for sentence categorization. Extensive experimentation using UMLS-based concept extraction, keyword selection, and Apriori-based itemset mining demonstrates that the proposed method significantly outperforms existing models in both the in formativeness and coherence of generated summaries. The results reveal that combining deep language models with domain-specific knowledge enhances summarization quality and can be effectively applied to diverse types of biomedical text.

Efficient Biomedical Text Summarization With Quantized LLaMA 2: Enhancing Memory Usage and Inference on Low Powered Devices

ABSTRACTThe deployment of large language models (LLMs) on edge devices and non‐server environments presents significant challenges, primarily due to constraints in memory usage, computational power, and inference time. This article investigates the feasibility of running LLMs across such devices by focusing on optimising memory usage, employing quantization techniques, and reducing inference time. Specifically, we utilise LLaMA 2 for biomedical text summarization and implement low‐rank adaptation (LoRA) quantization to compress the model size to compress the model size and fine‐tune it using limited resources. Our study systematically evaluates memory consumption during both training and inference phases, demonstrating substantial reductions through efficient LoRA quantization. Our results indicate that with careful optimization, it is feasible to deploy sophisticated LLMs like LLaMA 2 on low powered devices, thereby broadening the scope of their application in resource‐constrained environments.

Empowering legal justice with AI: A reinforcement learning SAC-VAE framework for advanced legal text summarization.

Automated summarization of legal texts poses a significant challenge due to the complex and specialized nature of legal documentation. Despite the recent progress in reinforcement learning for natural language text summarization, its application in the legal domain has been less effective. This paper introduces SAC-VAE, a novel reinforcement learning framework specifically designed for legal text summarization. We leverage a Variational Autoencoder (VAE) to condense the high-dimensional state space into a more manageable lower-dimensional feature space. These compressed features are subsequently utilized by the Soft Actor-Critic (SAC) algorithm for policy learning, facilitating the automated generation of summaries from legal texts. Through comprehensive experimentation, we have empirically demonstrated the effectiveness and superior performance of the SAC-VAE framework in legal text summarization.

Deep Learning Applications in Natural Language Processing and Optimization Strategies

In recent years, deep learning has made significant advancements in the field of natural language processing (NLP), driving the development of language understanding and generation tasks. Deep learning utilizes multi-layer neural networks to automatically extract and represent complex data features. This paper first introduces the basic principles of deep learning and mainstream frameworks (such as TensorFlow and PyTorch) and discusses core NLP tasks, including word embedding, language modeling, and text generation. Subsequently, it analyzes specific practices of deep learning applications in text classification, machine translation, automatic summarization, dialogue systems, and speech recognition. Further discussion covers model optimization methods, including structural optimization (such as RNN, LSTM, GRU, and Transformer), data preprocessing and feature engineering, hyperparameter tuning, and accelerated computation. Finally, it explores cutting-edge optimization strategies like federated learning, model compression, self-supervised learning, and transfer learning, proposing future research directions and challenges. This paper aims to systematically analyze the applications and optimization strategies of deep learning in NLP.

Single-Document Abstractive Text Summarization: A Systematic Literature Review

Abstractive text summarization is a task in natural language processing that automatically generates the summary from the source document in a human-written form with minimal loss of information. Research in text summarization has shifted towards abstractive text summarization due to its challenging aspects. This study provides a broad systematic literature review of abstractive text summarization on single-document summarization to gain insights into the challenges, widely used datasets, evaluation metrics, approaches, and methods. This study reviews research articles published between 2011 and 2023 from popular electronic databases. In total, 226 journal and conference publications were included in this review. The in-depth analysis of these papers helps researchers understand the challenges, widely used datasets, evaluation metrics, approaches, and methods. This paper identifies and discusses potential opportunities and directions, along with a generic conceptual framework and guidelines on abstractive summarization models and techniques for research in abstractive text summarization.

Enhancing pre-trained models for text summarization: a multi-objective genetic algorithm optimization approach

Enhancing pre-trained models for text summarization: a multi-objective genetic algorithm optimization approach

Contextual embedded text summarizer system: A hybrid approach

AbstractSelecting crucial sentences from a document is a pivotal task in automatic text summarization systems. Abstractive summarization involves rephrasing key content through advanced natural language techniques, generating a concise, new text conveying critical information. Conversely, extractive summarization reproduces important material from the original text. In the proposed method, a hybrid ensemble approach combines BERTsum for extractive summarization and Longformer2Roberta for abstractive summarization for generating a contextual semantic rich summary for a huge collection of text. These proposed system‐generated summaries were evaluated against reference summaries using the ROUGE package at three rouge levels (Rouge‐1, Rouge‐2, and Rouge‐L). The Proposed contextual embedded hybrid text summarization model has shown significant performance improvement in multiple levels of Rouge score and word mover distance (WMD) of generated summary with a reference summary. The proposed hybrid model demonstrates superior performance over existing state‐of‐the‐art summarizing models on three distinct datasets CNN dataset, WikiSum, and Gigaword dataset. The proposed hybrid model as a text summarizer involves leveraging its capabilities to process longer sequences of text with domain‐specific contextual summaries. This transformers‐based text summarization model has great potential in developing expert systems in various research domains such as health decision support systems, the education sector, customer support chatbots, financial analysis investment recommendations, and financial assistance.

HybridEval: An Improved Novel Hybrid Metric for Evaluation of Text Summarization

The present work re-evaluates the evaluation method for text summarization tasks. Two state-of-the-art assessment measures e.g., Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and Bilingual Evaluation Understudy (BLEU) are discussed along with their limitations before presenting a novel evaluation metric. The evaluation scores are significantly different because of the length and vocabulary of the sentences, this suggests that the primary restriction is its inability to preserve the semantics and meaning of the sentences and consistent weight distribution over the whole sentence. To address this, the present work organizes the phrases into six different groups and to evaluate “text summarization” problems, a new hybrid approach (HybridEval) is proposed. Our approach uses a weighted sum of cosine scores from InferSent’s SentEval algorithms combined with original scores, achieving high accuracy. HybridEval outperforms existing state-of-the-art models by 10-15% in evaluation scores.

Intelligent Abstractive Summarization of Scholarly Publications with Transfer Learning

Intelligent abstractive text summarization of scholarly publications refers to machine-generated summaries that capture the essential ideas of an article while maintaining semantic coherence and grammatical accuracy. As information continues to grow at an overwhelming rate, text summarization has emerged as a critical area of research. In the past, summarization of scientific publications predominantly relied on extractive methods. These approaches involve selecting key sentences or phrases directly from the original document to create a summary or generate a suitable title. Although extractive methods preserve the original wording, they often lack the ability to produce a coherent, concise, and fluent summary, especially when dealing with complex or lengthy texts. In contrast, abstractive summarization represents a more sophisticated approach. Rather than extracting content from the source, abstractive models generate summaries using new language, often incorporating words and phrases not found in the original text. This allows for more natural, human-like summaries that better capture the key ideas in a fluid and cohesive manner. This study introduces two advanced models for generating titles from the abstracts of scientific articles. The first model employs a Gated Recurrent Unit (GRU) encoder coupled with a greedy-search decoder, while the second utilizes a Transformer model, known for its capacity to handle long-range dependencies in text. The findings demonstrate that both models outperform the baseline Long Short-Term Memory (LSTM) model in terms of efficiency and fluency. Specifically, the GRU model achieved a ROUGE-1 score of 0.2336, and the Transformer model scored 0.2881, significantly higher than the baseline LSTM model, which reported a ROUGE-1 score of 0.1033. These results underscore the potential of abstractive models to enhance the quality and accuracy of summarization in academic and scholarly contexts, offering more intuitive and meaningful summaries.

Novel Genetic Optimization Techniques for Accurate Social Media Data Summarization and Classification Using Deep Learning Models

In the era of big data, effectively processing and understanding the vast quantities of brief texts on social media platforms like Twitter (X) is a significant challenge. This paper introduces a novel approach to automatic text summarization aimed at improving accuracy while minimizing redundancy. The proposed method involves a two-step process: first, feature extraction using term frequency–inverse document frequency (TF–IDF), and second, summary extraction through genetic optimized fully connected convolutional neural networks (GO-FC-CNNs). The approach was evaluated on datasets from the Kaggle collection, focusing on topics like FIFA, farmer demonstrations, and COVID-19, demonstrating its versatility across different domains. Preprocessing steps such as tokenization, stemming, stop word s removal, and keyword identification were employed to handle unprocessed data. The integration of genetic optimization into the neural network significantly improved performance compared to traditional methods. Evaluation using the ROUGE criteria showed that the proposed method achieved higher accuracy (98.00%), precision (98.30%), recall (98.72%), and F1-score (98.61%) than existing approaches. These findings suggest that this method can help create a reliable and effective system for large-scale social media data processing, enhancing data dissemination and decision-making.

Enhancing Text Summarization with Linguistic Prompting and Reinforcement Learning: A Human-Centered Approach

Enhancing Text Summarization with Linguistic Prompting and Reinforcement Learning: A Human-Centered Approach

Evaluation-Focused Multidimensional Score for Turkish Abstractive Text Summarization

Despite the inherent complexity of Abstractive Text Summarization, which is widely acknowledged as one of the most challenging tasks in the field of natural language processing, transformer-based models have emerged as an effective solution capable of delivering highly accurate and coherent summaries. In this study, the effectiveness of transformer-based text summarization models for Turkish language is investigated. For this purpose, we utilize BERTurk, mT5 and mBART as transformer-based encoder-decoder models. Each of the models was trained separately with MLSUM, TR-News, WikiLingua and Fırat_DS datasets. While obtaining experimental results, various optimizations were made in the summary functions of the models. Our study makes an important contribution to the limited Turkish text summarization literature by comparing the performance of different language models on existing Turkish datasets. We first evaluate ROUGE, BERTScore, FastText-based Cosine Similarity and Novelty Rate metrics separately for each model and dataset, then normalize and combine the scores we obtain to obtain a multidimensional score. We validate our innovative approach by comparing the summaries produced with the human evaluation results.

Drug and Vaccine Extractive Text Summarization Insights Using Fine-tuned Transformers

Text representation is a key aspect in determining the success of various text summarizing techniques. Summarization using pre-trained transformer models has produced encouraging results. Yet the scope of applying these models in medical and drug discovery is not examined to a proper extent. To address this issue, this article aims to perform extractive summarization based on fine-tuned transformers pertaining to drug and medical domain. This research also aims to enhance sentence representation. Exploring the extractive text summarization aspects of medical and drug discovery is a challenging task as the datasets are limited. Hence, this research concentrates on the collection of abstracts collected from PubMed for various domains of medical and drug discovery such as drug, COVID, etc.with a total capacity of 1,370 abstracts. A detailed experimentation using BART (Bidirectional Autoregressive Transformer), T5(Text-to-Text Transfer Transformer), LexRank and TexRank for the analysis of the dataset is carried out in this research to perform extractive text summarization.

A review of automatic source code summarization

A review of automatic source code summarization